A Learning-based Approach to Unit Testing of Numerical Software
2010 (English)In: 22nd IFIPInternational Conference on Testing Software and Systems, Natal, Brazil, Nov. 8-12, 2010 / [ed] Alexandre Petrenko, Adenilso da Silva Simão, José Carlos Maldonado, Berlin, Heidelberg: Springer , 2010, 221-235 p.Conference paper (Refereed)
We present an application of learning-based testing to theproblem of automated test case generation (ATCG) for numerical soft-ware. Our approach uses n-dimensional polynomial models as an algo-rithmically learned abstraction of the SUT which supports n-wise testing.Test cases are iteratively generated by applying a satisfiability algorithmto first-order program specifications over real closed fields and iterativelyrefined piecewise polynomial models.We benchmark the performance of our iterative ATCG algorithm againstiterative random testing, and empirically analyse its performance in find-ing injected errors in numerical codes. Our results show that for softwarewith small errors, or long mean time to failure, learning-based testing isincreasingly more efficient than iterative random testing.
Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer , 2010. 221-235 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 6435
Machine learning, Software Testing, Model Checking
IdentifiersURN: urn:nbn:se:kth:diva-40870DOI: 10.1007/978-3-642-16573-3_16ISI: 000289226300016ScopusID: 2-s2.0-78649885303ISBN: 978-3-642-16572-6OAI: oai:DiVA.org:kth-40870DiVA: diva2:442572